plot.gam {mgcv} | R Documentation

## Default GAM plotting

### Description

Takes a fitted `gam` object produced by `gam()` and plots the component smooth functions that make it up, on the scale of the linear predictor.

### Usage

    plot.gam(x,rug=TRUE,se=TRUE,pages=0,select=NULL,scale=-1,n=100,n2=40,
             pers=FALSE,theta=30,phi=30,jit=FALSE,...)

### Arguments

`x` | a fitted `gam` object as produced by `gam()`.
`rug` | when TRUE (default) then the covariate to which the plot applies is displayed as a rug plot at the foot of each plot of a 1-d smooth, and the locations of the covariates are plotted as points on the contour plot representing a 2-d smooth.

`se`

when TRUE (default) upper and lower lines are added to the 1-d plots at 2 standard errors above and below the estimate of the smooth being plotted while for 2-d plots, surfaces at +1 and -1 standard errors are contoured and overlayed on the contour plot for the estimate. If a positive number is supplied then this number is multiplied by the standard errors when calculating standard error curves or surfaces.

`pages` | (default 0) the number of pages over which to spread the output. For example, if `pages=1` then all terms will be plotted on one page with the layout performed automatically. Set to 0 to have the routine leave all graphics settings as they are.
`select` | Allows the plot for a single model term to be selected for printing. e.g. if you just want the plot for the second smooth term set `select=2`.
`scale` | set to -1 (default) to have the same y-axis scale for each plot, and to 0 for a different y axis for each plot.
`n` | number of points used for each 1-d plot - for a nice smooth plot this needs to be several times the estimated degrees of freedom for the smooth. Default value 100.
`n2` | Square root of number of points used to grid estimates of 2-d functions for contouring.
`pers` | Set to `TRUE` if you want perspective plots for 2-d terms.
`theta` | One of the perspective plot angles.
`phi` | The other perspective plot angle.
`jit` | Set to TRUE if you want rug plots for 1-d terms to be jittered.
`...` | other arguments.

### Details

Produces default plot showing the smooth components of a fitted GAM.

For plots of 1-d smooths, the x axis of each plot is labelled with the covariate name, while the y axis is labelled `s(cov,edf)` where `cov` is the covariate name, and `edf` the estimated (or user defined for regression splines) degrees of freedom of the smooth.

Contour plots are produced for 2-d smooths with the x-axes labelled with the first covariate name and the y axis with the second covariate name. The main title of the plot is something like `s(var1,var2,edf)`, indicating the variables of which the term is a function, and the estimated degrees of freedom for the term. When `se=TRUE`, estimator variability is shown by overlaying contour plots at plus and minus 1 s.e. relative to the main estimate. If `se` is a positive number then contour plots are at plus or minus `se` multiplied by the s.e. Contour levels are chosen to try and ensure reasonable separation of the contours of the different plots, but this is not always easy to achieve.

Within the function, the data for the plots is obtained by direct calls to the compiled C code that `predict.gam` uses.

Smooths of more than 2 variables are not currently dealt with, but simply generate a warning, but see `vis.gam`.

### Value

The function simply generates plots.

### WARNING

Note that the behaviour of this function is not identical to `plot.gam()` in Splus.

The function can not deal with smooths of more than 2 variables!

### Author(s)

Simon N. Wood simon@stats.gla.ac.uk

### References

Gu and Wahba (1991) Minimizing GCV/GML scores with multiple smoothing parameters via the Newton method. SIAM J. Sci. Statist. Comput. 12:383-398

Wood, S.N. (2000) Modelling and Smoothing Parameter Estimation with Multiple Quadratic Penalties. J.R.Statist.Soc.B 62(2):413-428

Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B 65(1):95-114

http://www.stats.gla.ac.uk/~simon/

### See Also

`gam`, `predict.gam`, `vis.gam`

### Examples

    library(mgcv)
    set.seed(0)
    n<-200
    sig2<-4
    x0 <- runif(n, 0, 1)
    x1 <- runif(n, 0, 1)
    x2 <- runif(n, 0, 1)
    x3 <- runif(n, 0, 1)
    pi <- asin(1) * 2
    y <- 2 * sin(pi * x0)
    y <- y + exp(2 * x1) - 3.75887
    y <- y + 0.2 * x2^11 * (10 * (1 - x2))^6 + 10 * (10 * x2)^3 * (1 - x2)^10 - 1.396
    e <- rnorm(n, 0, sqrt(abs(sig2)))
    y <- y + e
    b<-gam(y~s(x0)+s(x1)+s(x2)+s(x3))
    plot(b,pages=1)
    # example with 2-d plots
    b1<-gam(y~s(x0,x1)+s(x2)+s(x3))
    op<-par(mfrow=c(2,2))
    plot(b1)
    par(op)

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